Work

Advanced ANN Architecture for the CTU Partitioning in All Intra HEVC

Year: 2025

Type: article

Abstract: Due to the growing complexity of video encoders, the optimization of the parameters of the encoding process is becoming an important issue. In recent years, this has become an important field of application of neural networks. Artificial neural networks in video encoders are used to accelerate the video encoder operation. This paper demonstrates the use of different ResNet- and DenseNet-type architectures to accelerate the CTU partitioning algorithm in HEVC in All Intra mode. The paper demonstrates the results of an exhaustive evaluation of different proposed architectures, considering compression efficiency, network size, and encoding time reduction. Multiple pros and cons of the proposed architectures are presented in the Conclusions, considering various limitations that may be important for a given application, like hardware-constrained sensor networks or standalone small devices operating with images and videos.

Source: Sensors

DOI: 10.3390/s25195971

Authors: Jakub Kwaśniak, Mateusz Majtka, Mateusz Lorkiewicz, Tomasz Grajek, Krzysztof Klimaszewski

Institution: Poznań University of Technology

Language: English

Topic: Video Coding and Compression Technologies

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Ministerstwo Edukacji i Nauki